scholarly journals Nasal Expiration Image Detection on Mirror Surface Using Alternating Optimization

2019 ◽  
Author(s):  
Kosuke Nakahara ◽  
Kazu Mishiba ◽  
Rieko Doi ◽  
Katsuya Kondo
Keyword(s):  
Mirror Surface ◽  
Image Detection ◽  
Alternating Optimization

Joint power and duty-cycle design using alternating optimization algorithm under energy harvesting architectures

2020 ◽  
Vol 17 (12) ◽  
pp. 139-155
Author(s):  
Tong Wang ◽  
Xiang Yang ◽  
Feng Deng ◽  
Lin Gao ◽  
Yufei Jiang ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Optimization Algorithm ◽  
Duty Cycle ◽  
Joint Power ◽  
Alternating Optimization

Inappropriate thermal image detection system using machine learning

2020 ◽  
Vol 14 (2) ◽  
pp. 102-111
Author(s):  
S. W. Kwon ◽  
I. S. Song ◽  
S. W. Lee ◽  
J. S. Lee ◽  
J. H. Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Thermal Image ◽  
Image Detection

scholarly journals VIPPrint: Validating Synthetic Image Detection and Source Linking Methods on a Large Scale Dataset of Printed Documents

2021 ◽  
Vol 7 (3) ◽  
pp. 50
Author(s):  
Anselmo Ferreira ◽  
Ehsan Nowroozi ◽  
Mauro Barni
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Child Pornography ◽  
Forensic Analysis ◽  
Synthetic Image ◽  
Image Detection ◽  
Face Images ◽  
Large Scale Dataset ◽  
Scanned Images ◽  
Analysis Of The Images

The possibility of carrying out a meaningful forensic analysis on printed and scanned images plays a major role in many applications. First of all, printed documents are often associated with criminal activities, such as terrorist plans, child pornography, and even fake packages. Additionally, printing and scanning can be used to hide the traces of image manipulation or the synthetic nature of images, since the artifacts commonly found in manipulated and synthetic images are gone after the images are printed and scanned. A problem hindering research in this area is the lack of large scale reference datasets to be used for algorithm development and benchmarking. Motivated by this issue, we present a new dataset composed of a large number of synthetic and natural printed face images. To highlight the difficulties associated with the analysis of the images of the dataset, we carried out an extensive set of experiments comparing several printer attribution methods. We also verified that state-of-the-art methods to distinguish natural and synthetic face images fail when applied to print and scanned images. We envision that the availability of the new dataset and the preliminary experiments we carried out will motivate and facilitate further research in this area.

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